code: def load_task(task_id):
      """Function used for loading data."""
      task = openml.tasks.get_task(task_id)
      X, y = task.get_X_and_y()
      train_indices, test_indices = task.get_train_test_split_indices()
      X_train = X[train_indices]
      y_train = y[train_indices]
      X_test = X[test_indices]
      y_test = y[test_indices]
      dataset = openml.datasets.get_dataset(task.dataset_id)
      _, _, cat = dataset.get_data(
          return_categorical_indicator=True, target=task.target_name
      )
      del _
      del dataset
      cat = ["categorical" if c else "numerical" for c in cat]
  
      unique = np.unique(y_train)
      mapping = {
          unique_value: i for i, unique_value in enumerate(unique)
      }
      y_train = np.array([mapping[value] for value in y_train])
      y_test = np.array([mapping[value] for value in y_test])
  
      return X_train, y_train, X_test, y_test, cat
  
file_path: auto-sklearn/scripts/2015_nips_paper/run/run_auto_sklearn.py
imports: import os
  import argparse
  import numpy
  import openml
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.metrics import balanced_accuracy
  from remove_dataset_from_metadata import remove_dataset
  import score_ensemble
label_desc: Use image node_id 1 for calling a global function with example usage: load_task(task_id) and returns: X_train,  y_train,  X_test,  y_test,  cat